Training trees on tails with applications to portfolio choice

DOI10.1007/s10479-020-03539-2zbMath1436.62489OpenAlexW2980995637MaRDI QIDQ2173122

Guillaume Coqueret, Tony Guida

Publication date: 22 April 2020

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-020-03539-2

zbMATH Keywords

decision treesportfolio choicefeature selectionfactor investingfiltering training set

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Economic time series analysis (91B84) Compound decision problems in statistical decision theory (62C25)

Uses Software

• XGBoost

Cites Work

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• Tree-based censored regression with applications in insurance
• An interactive approach to stochastic programming-based portfolio optimization
• A copula-based scenario tree generation algorithm for multiperiod portfolio selection problems
• Improving the precision of classification trees
• Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S\&P 500
• Selecting rows and columns for training support vector regression models with large retail datasets
• Large data sets and machine learning: applications to statistical arbitrage
• Active Learning
• 10.1162/153244303322753616
• An Example of an Improvable Rao–Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator
• Optimal classification trees

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